Antibiotic persistence remains one of the most challenging barriers to effective clearance of chronic bacterial infections. Despite the intervenin decades since its original discovery, we still lack a basic understanding of how molecular networks implement the defining features of persistence: phenotypic heterogeneity and tolerance to lethal levels of antibiotic exposure. Our work during the previous funding period, and recent observations by other groups, present a much more complex picture of persistence than previously thought. Building on our preliminary results, we propose to apply an integrated experimental/computational approach in order to achieve a new systems-level understanding of persistence. We aim to accomplish this by discovering the most comprehensive set of genetic loci involved, mapping out the genetic network context in which these loci function and to reverse engineer the regulatory networks that reside at the core of persistence. These analyses will be aided by a systematic search for robust markers of the persistence cellular state. These markers will then be used to FACS-isolate sub-populations of cells exhibiting extremes of persistence. Each of these sub-populations will then be interrogated for the most comprehensive set of internal molecular states and regulatory interactions, utilizing a combination of existing methods and genomic technologies recently developed by our group. These include: transcriptomics, genome-wide in vivo DNA-protein interactions, transcriptome-wide in vivo RNA-protein interactions, RNA-RNA interactions, and ribosome-RNA interactions. The massive scale of these observations will reveal the most comprehensive and unbiased global view of the persistence cellular state, to date. Through computational analysis and integration of these observations we aim to learn compact graphical models of causal regulatory interactions that modulate persistence. These analyses will utilize powerful algorithms developed by our group, including information-theoretic pathway analysis, cis-regulatory analysis of linear (DNA/RNA) and structural RNA regulatory elements, along with Bayesian evidence integration. Beyond achieving a systems-level understanding of persistence, the composition and architecture of these molecular networks will serve as a knowledge-scaffold for development of novel strategies for eradicating chronic bacterial infections.